Correlation of image textures of a polarization feature parameter and the microstructures of liver fibrosis tissues
Abstract
Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures. Liver fibrosis is a characteristic of many types of chronic liver diseases. The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures. The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis. Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues. In this study, a fiber-sensitive polarization feature parameter (PFP) was derived through the forward sequential feature selection (SFS) and linear discriminant analysis (LDA) to target on the identification of fibrous structures. Then, the Pearson correlation coefficients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated. The results show the gray level run length matrix (GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919. The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures. This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis.
References
- 1. , “Global epidemiology of chronic liver disease,” Clin. Liver Dis. 17, 365 (2021). Crossref, Google Scholar
- 2. , “Burden of liver diseases in the world,” J. Hepatol. 70, 151–171 (2019). Crossref, Web of Science, Google Scholar
- 3. , “Hematoxylin and eosin staining of tissue and cell sections,” Cold Spring Harb. Protoc. 2008, pdb-prot4986 (2008). Crossref, Google Scholar
- 4. , “The Masson trichrome staining methods in routine laboratory use,” Stain Technol. 8, 101–110 (1933). Crossref, Google Scholar
- 5. , “A simple method for the silver impregnation of reticulum,” Am. J. Pathol. 12, 545 (1936). Google Scholar
- 6. , “Mueller matrix polarimetry — an emerging new tool for characterizing the microstructural feature of complex biological specimen,” J. Lightw. Technol. 37, 2534–2548 (2019). Crossref, Web of Science, Google Scholar
- 7. , “Polarisation optics for biomedical and clinical applications: A review,” Light: Sci. Appl. 10, 1–20 (2021). Crossref, Web of Science, Google Scholar
- 8. , “A high definition Mueller polarimetric endoscope for tissue characterisation,” Sci. Rep. 6, 1–11 (2016). Web of Science, Google Scholar
- 9. , “Mueller polarimetric imaging for surgical and diagnostic applications: A review,” J. Biophoton. 10, 950–982 (2017). Crossref, Web of Science, Google Scholar
- 10. , “Distinguishing structural features between Crohns disease and gastrointestinal luminal tuberculosis using Mueller matrix derived parameters,” J. Biophoton. 12, e201900151 (2019). Crossref, Web of Science, Google Scholar
- 11. , “Visualization of white matter fiber tracts of brain tissue sections with wide-field imaging Mueller polarimetry,” IEEE Trans. Med. Imaging 39, 4376–4382 (2020). Crossref, Web of Science, Google Scholar
- 12. , “Mueller matrix imaging for collagen scoring in mice model of pregnancy,” Sci. Rep. 11, 1–12 (2021). Web of Science, Google Scholar
- 13. , “Quantitative Mueller matrix polarimetry techniques for biological tissues,” J. Innov. Opt. Health Sci. 5, 1250017 (2012). Link, Web of Science, Google Scholar
- 14. , “Identification of serous ovarian tumors based on polarization imaging and correlation analysis with clinicopathological features,” J. Innov. Opt. Health Sci. 2241002 (2022). Web of Science, Google Scholar
- 15. , “Quantitatively characterizing the microstructural features of breast ductal carcinoma tissues in different progression stages by Mueller matrix microscope,” Biomed. Opt. Express 8, 3643–3655 (2017). Crossref, Web of Science, Google Scholar
- 16. , “Mueller polarimetric imaging system with liquid crystals,” Appl. Opt. 43, 2824–2832 (2004). Crossref, Web of Science, Google Scholar
- 17. , “Mueller matrix microscope: A quantitative tool to facilitate detections and fibrosis scorings of liver cirrhosis and cancer tissues,” J. Biomed. Opt. 21, 071112 (2016). Crossref, Web of Science, Google Scholar
- 18. , “Digital histology with Mueller microscopy: How to mitigate an impact of tissue cut thickness fluctuations,” J. Biomed. Opt. 24, 076004 (2019). Crossref, Web of Science, Google Scholar
- 19. , “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011). Web of Science, Google Scholar
- 20. , Hands-on Machine Learning with ScikitLearn, Keras, and TensorFlow: Concepts, Tools,and Techniques to Build Intelligent Systems (O’Reilly Media, Sebastopol, CA, USA, 2019), p. 235. Google Scholar
- 21. , “
Comparative study of techniques for large-scale feature selection ,” in Machine Intelligence and Pattern Recognition (Elsevier, North-Holland, 1994), pp. 403–413. Google Scholar - 22. , “Liver biopsy assessment in chronic viral hepatitis: A personal, practical approach,” Mod. Pathol. 20, S3–S14 (2007). Crossref, Web of Science, Google Scholar
- 23. , “Fast Mueller matrix microscope based on dual DoFP polarimeters,” Opt. Lett. 46, 1676–1679 (2021). Crossref, Web of Science, Google Scholar
- 24. , “Interpolation strategies for reducing IFOV artifacts in microgrid polarimeter imagery,” Opt. Express 17, 9112–9125 (2009). Crossref, Web of Science, Google Scholar
- 25. , “Polaromics: Deriving polarization parameters from a Mueller matrix for quantitative characterization of biomedical specimen,” J. Phys. D: Appl. Phys. (2021). Web of Science, Google Scholar
- 26. , “Separating azimuthal orientation dependence in polarization measurements of anisotropic media,” Opt. Express 26, 3791–3800 (2018). Crossref, Web of Science, Google Scholar
- 27. , “Interpretation of Mueller matrices based on polar decomposition,” J. Opt. Soc. Am. A 13, 1106–1113 (1996). Crossref, Google Scholar
- 28. , “Mueller matrix approach for determination of optical rotation in chiral turbid media in backscattering geometry,” Opt. Express 14, 190–202 (2006). Crossref, Web of Science, Google Scholar
- 29. , “Mueller matrix decomposition for extraction of individual polarization parameters from complex turbid media exhibiting multiple scattering, optical activity, and linear birefringence,” J. Biomed. Opt. 13, 044036 (2008). Crossref, Web of Science, Google Scholar
- 30. , “A polarization-imaging-based machine learning framework for quantitative pathological diagnosis of cervical precancerous lesions,” IEEE Trans. Med. Imaging 40, 3728–3738 (2021). Crossref, Web of Science, Google Scholar
- 31. , “Polarization entropy transfer and relative polarization entropy,” Opt. Commun. 123, 443–448 (1996). Crossref, Web of Science, Google Scholar
- 32. , “Physically realizable space for the purity-depolarization plane for polarized light scattering media,” Phys. Rev. Lett. 119, 033202 (2017). Crossref, Web of Science, Google Scholar
- 33. , “Depolarization and polarization indices of an optical system,” Opt. Acta: Int. J. Opt. 33, 185–189 (1986). Crossref, Google Scholar
- 34. , “Polarimetry feature parameter deriving from Mueller matrix imaging and auto-diagnostic significance to distinguish HSIL and CSCC,” J. Innov. Opt. Health Sci. 15, 2142008 (2022). Link, Web of Science, Google Scholar
- 35. A. Zwanenburg, S. Leger, M. Vallières, S. Löck, “Image biomarker standardisation initiative,” arXiv:1612.07003. Google Scholar
- 36. , “Array programming with NumPy,” Nature 585, 357–362 (2020). Crossref, Web of Science, Google Scholar
- 37. , “Tests and measurements: The T-test,” Strength Cond. J. 12, 36–37 (1990). Crossref, Google Scholar
- 38. , “T-test as a parametric statistic,” Korean J. Anesthesiol. 68, 540 (2015). Crossref, Google Scholar
- 39. , “Anisotropy coefficients of a Mueller matrix,” J. Opt. Soc. Am. A 28, 548–553 (2011). Crossref, Google Scholar
- 40. , “Substrate-induced chirality in an individual nanostructure,” ACS Photon. 6, 1876–1881 (2019). Crossref, Web of Science, Google Scholar
- 41. , “Characterizing the microstructures of biological tissues using Mueller matrix and transformed polarization parameters,” Biomed. Opt. Express 5, 4223–4234 (2014). Crossref, Web of Science, Google Scholar
- 42. , “Polarization imaging feature characterization of different endometrium phases by machine learning,” OSA Contin. 4, 1776–1791 (2021). Crossref, Web of Science, Google Scholar
- 43. , “Computational radiomics system to decode the radiographic phenotype,” Cancer Res. 77, e104–e107 (2017). Crossref, Web of Science, Google Scholar
- 44. , Introduction to Linear Regression Analysis (John Wiley & Sons, Hoboken, New Jersey, USA, 2021), pp. 12–35. Google Scholar
- 45. , Linear Regression Analysis (John Wiley & Sons, 2012). Google Scholar